analytics VS airbyte

Take a look at gitlab's dbt project: https://gitlab.com/gitlab-data/analytics/-/blob/master/transform/snowflake-dbt/models/common/schema.yml

A good reference is the Gitlab data team repo. https://gitlab.com/gitlab-data/analytics

For example, their Data Team have a public repository, with a bunch of information on how they organize DAGs, machine learning projects, system configuration, etc.

So I've run through the DBT tutorial stuff and looked over some fairly complex uses of it i.e. GitLab Data and I was wondering if anyone has any opinions or insights into the use of jinja templating in the sql?

Gitlab has their DBT repo open source and is very useful for seeing how to structure a project at scale. https://gitlab.com/gitlab-data/analytics/-/tree/master/transform/snowflake-dbt

Currently the team is working hard on this: https://gitlab.com/gitlab-data/analytics/-/issues/9508

GitLab's dbt project is an excellent example of a mature project at scale. They also have a comprehensive guide to their methodology.

I'l also give a shout-out to Airbyte (https://airbyte.com/), with which I've had some limited success with integrating Salesforce to a local database. The particular pull for Airbyte is that we can self-host the open source version, rather than pay Fivetran a significant sum to do this for us.

It's an immature tool, so I don't yet know that I can claim we've spent _less_ than Fivetran on the additional engineering and ops time, but it feels like it has potential to do so once stabilized.

Link to GitHub -->

When it says "available in open source", is that under the main airbyte repo's licensing [1], hence primarily licensed under the Elastic License v2 and therefore not typically considered open source by many?

Airbyte has previous of advertising their offering as open source while not really being as per the OSD[2]. This has been raised with them previously but without response [3][4]. They've also been extending their use of ELv2, recently moving many of their existing MIT licensed connectors to be ELv2 [5].

[1] https://github.com/airbytehq/airbyte/blob/master/LICENSE

As possible solution, I can suggest Airbyte(https://airbyte.com/). it's more performant than generic python script.

Airbyte, an open-source data integration engine that offers hundreds of connectors with data warehouses and databases, has gained popularity for its seamless integration and data syncing capabilities. Xata's integration with Airbyte offers a streamlined data ingestion process from any Airbyte input source directly into your Xata database.

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte